File size: 4,769 Bytes
170658b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | """A deliberately tiny GPT-style language model for CPU experiments."""
from __future__ import annotations
import torch
import torch.nn as nn
from torch.nn import functional as F
class TinyGPTConfig:
def __init__(
self,
vocab_size: int,
block_size: int = 64,
n_layer: int = 2,
n_head: int = 2,
n_embd: int = 64,
dropout: float = 0.1,
):
self.vocab_size = vocab_size
self.block_size = block_size
self.n_layer = n_layer
self.n_head = n_head
self.n_embd = n_embd
self.dropout = dropout
class CausalSelfAttention(nn.Module):
def __init__(self, cfg: TinyGPTConfig):
super().__init__()
assert cfg.n_embd % cfg.n_head == 0
self.n_head = cfg.n_head
self.head_dim = cfg.n_embd // cfg.n_head
self.qkv = nn.Linear(cfg.n_embd, 3 * cfg.n_embd)
self.proj = nn.Linear(cfg.n_embd, cfg.n_embd)
self.dropout = nn.Dropout(cfg.dropout)
self.register_buffer(
"mask",
torch.tril(torch.ones(cfg.block_size, cfg.block_size)).view(1, 1, cfg.block_size, cfg.block_size),
persistent=False,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
b, t, c = x.shape
q, k, v = self.qkv(x).split(c, dim=2)
q = q.view(b, t, self.n_head, self.head_dim).transpose(1, 2)
k = k.view(b, t, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(b, t, self.n_head, self.head_dim).transpose(1, 2)
att = (q @ k.transpose(-2, -1)) * (self.head_dim ** -0.5)
att = att.masked_fill(self.mask[:, :, :t, :t] == 0, float("-inf"))
att = F.softmax(att, dim=-1)
att = self.dropout(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(b, t, c)
return self.dropout(self.proj(y))
class Block(nn.Module):
def __init__(self, cfg: TinyGPTConfig):
super().__init__()
self.ln1 = nn.LayerNorm(cfg.n_embd)
self.attn = CausalSelfAttention(cfg)
self.ln2 = nn.LayerNorm(cfg.n_embd)
self.mlp = nn.Sequential(
nn.Linear(cfg.n_embd, 4 * cfg.n_embd),
nn.GELU(),
nn.Linear(4 * cfg.n_embd, cfg.n_embd),
nn.Dropout(cfg.dropout),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class TinyGPT(nn.Module):
def __init__(self, cfg: TinyGPTConfig):
super().__init__()
self.cfg = cfg
self.token_embedding = nn.Embedding(cfg.vocab_size, cfg.n_embd)
self.position_embedding = nn.Embedding(cfg.block_size, cfg.n_embd)
self.drop = nn.Dropout(cfg.dropout)
self.blocks = nn.Sequential(*[Block(cfg) for _ in range(cfg.n_layer)])
self.ln_f = nn.LayerNorm(cfg.n_embd)
self.head = nn.Linear(cfg.n_embd, cfg.vocab_size, bias=False)
# Weight tying: common in GPT-style LMs.
self.head.weight = self.token_embedding.weight
self.apply(self._init_weights)
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx: torch.Tensor, targets: torch.Tensor | None = None):
b, t = idx.shape
if t > self.cfg.block_size:
raise ValueError(f"sequence length {t} > block_size {self.cfg.block_size}")
pos = torch.arange(0, t, device=idx.device)
x = self.token_embedding(idx) + self.position_embedding(pos)[None, :, :]
x = self.drop(x)
x = self.blocks(x)
x = self.ln_f(x)
logits = self.head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
return logits, loss
@torch.no_grad()
def generate(self, idx: torch.Tensor, max_new_tokens: int, temperature: float = 0.8, top_k: int | None = None):
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.cfg.block_size :]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / max(temperature, 1e-6)
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float("inf")
probs = F.softmax(logits, dim=-1)
next_idx = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, next_idx), dim=1)
return idx
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